ColBERT：在并行神经网络中使用BERT句子嵌入进行计算幽默识别
ColBERT: Using BERT Sentence Embedding in Parallel Neural Networks for Computational Humor
AI总结：
提出基于BERT句子嵌入和并行神经网络的幽默检测方法，利用语言学理论，在20万数据集上F1达0.982，优于现有模型。
摘要：
幽默检测和评级的自动化在现代技术中具有有趣的应用场景，例如人形机器人、聊天机器人和虚拟助手。在本文中，我们基于一种流行的幽默语言学理论，提出了一种新颖的方法来检测和评级短文本中的幽默。所提出的技术方法首先将给定文本的句子分离，并利用BERT模型为每个句子生成嵌入。这些嵌入被输入到神经网络中独立的隐藏层线路（每个句子对应一条线路）以提取潜在特征。最后，并行线路被拼接起来以确定句子之间的一致性及其他关系，并预测目标值。我们随论文附带了一个新颖的幽默检测数据集，包含200,000个正式的短文本。除了在新数据集上评估我们的工作外，我们还参加了一场专注于西班牙语推文幽默评级的实时机器学习竞赛。所提出的模型在幽默检测实验中获得了0.982和0.869的F1分数，优于一般模型和最先进的模型。在两个对比环境下的评估证实了该模型的强度和鲁棒性，并指出了在当前任务中实现高准确率的两个重要因素：1）使用句子嵌入；2）在设计所提出模型时利用幽默的语言学结构。
英文摘要：
Automation of humor detection and rating has interesting use cases in modern technologies, such as humanoid robots, chatbots, and virtual assistants. In this paper, we propose a novel approach for detecting and rating humor in short texts based on a popular linguistic theory of humor. The proposed technical method initiates by separating sentences of the given text and utilizing the BERT model to generate embeddings for each one. The embeddings are fed to separate lines of hidden layers in a neural network (one line for each sentence) to extract latent features. At last, the parallel lines are concatenated to determine the congruity and other relationships between the sentences and predict the target value. We accompany the paper with a novel dataset for humor detection consisting of 200,000 formal short texts. In addition to evaluating our work on the novel dataset, we participated in a live machine learning competition focused on rating humor in Spanish tweets. The proposed model obtained F1 scores of 0.982 and 0.869 in the humor detection experiments which outperform general and state-of-the-art models. The evaluation performed on two contrasting settings confirm the strength and robustness of the model and suggests two important factors in achieving high accuracy in the current task: 1) usage of sentence embeddings and 2) utilizing the linguistic structure of humor in designing the proposed model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
